The exponential advancement of quantum computing has led to its increasing integration into medical radiology. Quantum-inspired algorithms have helped accelerate magnetic resonance fingerprinting for possible applications in clinic settings. Numerous global initiatives are currently integrating quantum computing into medical radiology and health care applications. Given the potential of quantum computing to enhance clinical care and medical research, we have developed this primer to introduce medical physicists to the realm of quantum computing. In this primer, we explore the application of currently available quantum computing-based auto-contouring methods to image segmentation. These implementations serve as prototypes of existing quantum algorithms tailored for specific quantum hardware, specifically focusing on the auto-contouring of medical imaging. We evaluated these algorithms using a small MRI abdominal dataset comprising 102 patient scans. Our findings suggest that quantum computing for auto-contouring is still in its infancy, with artificial intelligence-based algorithms remaining the preferred choice for auto- contouring in treatment planning.
","abstract_html":"<p>The exponential advancement of quantum computing has led to its increasing integration into medical radiology. Quantum-inspired algorithms have helped accelerate magnetic resonance fingerprinting for possible applications in clinic settings. Numerous global initiatives are currently integrating quantum computing into medical radiology and health care applications. Given the potential of quantum computing to enhance clinical care and medical research, we have developed this primer to introduce medical physicists to the realm of quantum computing. In this primer, we explore the application of currently available quantum computing-based auto-contouring methods to image segmentation. These implementations serve as prototypes of existing quantum algorithms tailored for specific quantum hardware, specifically focusing on the auto-contouring of medical imaging. We evaluated these algorithms using a small MRI abdominal dataset comprising 102 patient scans. Our findings suggest that quantum computing for auto-contouring is still in its infancy, with artificial intelligence-based algorithms remaining the preferred choice for auto- contouring in treatment planning.</p>","abstract_has_math":false,"creators":["Glenn, Rachel",""],"institution":null,"degree_name":"Masters of Science (MS)","degree_level":"Thesis (MS)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["David T. Fuentes","James A. Bankson","Jason Stafford"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-01T07:00:00Z","date_published":"2024-05-01T07:00:00Z","updated_at":"2026-07-24T05:49:08Z","subjects":["Quantum Computing","Medical Image Processing","Image Segmentation","autocontouring","Other Physics","Quantum Physics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1348","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["David T. Fuentes","James A. Bankson","Jason Stafford"]},{"key":"dc:creator","label":"Author","values":["Glenn, Rachel",""]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-24T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (MS)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Masters of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Quantum Computing","Medical Image Processing","Image Segmentation","autocontouring","Other Physics","Quantum Physics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1348"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The exponential advancement of quantum computing has led to its increasing integration into medical radiology. Quantum-inspired algorithms have helped accelerate magnetic resonance fingerprinting for possible applications in clinic settings. Numerous global initiatives are currently integrating quantum computing into medical radiology and health care applications. Given the potential of quantum computing to enhance clinical care and medical research, we have developed this primer to introduce medical physicists to the realm of quantum computing. In this primer, we explore the application of currently available quantum computing-based auto-contouring methods to image segmentation. These implementations serve as prototypes of existing quantum algorithms tailored for specific quantum hardware, specifically focusing on the auto-contouring of medical imaging. We evaluated these algorithms using a small MRI abdominal dataset comprising 102 patient scans. Our findings suggest that quantum computing for auto-contouring is still in its infancy, with artificial intelligence-based algorithms remaining the preferred choice for auto- contouring in treatment planning.
"]},{"key":"dc:title","label":"Title","values":["Quantum Computing Based Image segmentation for Treatment Planning Applications"]}]}],"canonical_facts":{"dc:contributor":["David T. Fuentes","James A. Bankson","Jason Stafford"],"dc:creator":["Glenn, Rachel",""],"dc:date.available":["2025-04-24T07:00:00Z"],"dc:description.abstract":["The exponential advancement of quantum computing has led to its increasing integration into medical radiology. Quantum-inspired algorithms have helped accelerate magnetic resonance fingerprinting for possible applications in clinic settings. Numerous global initiatives are currently integrating quantum computing into medical radiology and health care applications. Given the potential of quantum computing to enhance clinical care and medical research, we have developed this primer to introduce medical physicists to the realm of quantum computing. In this primer, we explore the application of currently available quantum computing-based auto-contouring methods to image segmentation. These implementations serve as prototypes of existing quantum algorithms tailored for specific quantum hardware, specifically focusing on the auto-contouring of medical imaging. We evaluated these algorithms using a small MRI abdominal dataset comprising 102 patient scans. Our findings suggest that quantum computing for auto-contouring is still in its infancy, with artificial intelligence-based algorithms remaining the preferred choice for auto- contouring in treatment planning.
"],"dc:identifier":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1348"],"dc:subject":["Quantum Computing","Medical Image Processing","Image Segmentation","autocontouring","Other Physics","Quantum Physics"],"dc:title":["Quantum Computing Based Image segmentation for Treatment Planning Applications"],"thesis:degree_level":["Thesis (MS)"],"thesis:degree_name":["Masters of Science (MS)"]},"updated_at":"2026-07-24T05:49:08Z"}